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Adversarial patch attacks are among one of the most practical threat models against real-world computer vision systems.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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Tom B Brown, Dandelion Mané, Aurko Roy, Martín Abadi, and Justin Gilmer · 2017
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A rotation and a translation suffice: Fooling cnns with simple transformations
Logan Engstrom, Brandon Tran, Dimitris Tsipras, Ludwig Schmidt, and Aleksander Madry · 2017
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
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Adversarial generative nets: Neural network attacks on state-of-the-art face recognition
Mahmood Sharif, Sruti Bhagavatula, Lujo Bauer, and Michael K Reiter · 2017
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Provable defenses against adversarial examples via the convex outer adversarial polytope
Eric Wong and J Zico Kolter · 2017
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Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Anish Athalye, Nicholas Carlini, and David Wagner · 2018
Cited alongside, same era.
On the effectiveness of interval bound propagation for training verifiably robust models
Sven Gowal, Krishnamurthy Dvijotham, Robert Stanforth, Rudy Bunel, Chongli Qin, Jonathan Uesato, Timothy Mann, and Pushmeet Kohli · 2018
Cited alongside, same era.
On visible adversarial perturbations & digital watermarking
Jamie Hayes · 2018
Cited alongside, same era.
Lavan: Localized and visible adversarial noise
Danny Karmon, Daniel Zoran, and Yoav Goldberg · 2018
Cited alongside, same era.
Differentiable abstract interpretation for provably robust neural networks
Matthew Mirman, Timon Gehr, and Martin Vechev · 2018
Robustness certificates for sparse adversarial attacks by randomized ablation
Alexander Levine and Soheil Feizi · 2019
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Sparsefool: a few pixels make a big difference
Apostolos Modas, Seyed-Mohsen Moosavi-Dezfooli, and Pascal Frossard · 2019
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Local gradients smoothing: Defense against localized adversarial attacks
Muzammal Naseer, Salman Khan, and Fatih Porikli · 2019
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Adversarial training for free!
Ali Shafahi, Mahyar Najibi, Amin Ghiasi, Zheng Xu, John Dickerson, Christoph Studer, Larry S Davis, Gavin Taylor, and Tom Goldstein · 2019
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Adversarial training and robustness for multiple perturbations
Florian Tramèr and Dan Boneh · 2019
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Cited alongside, same era.
Are adversarial examples inevitable?
Ali Shafahi, W Ronny Huang, Christoph Studer, Soheil Feizi, and Tom Goldstein · 2018
Cited alongside, same era.
Certified adversarial robustness via randomized smoothing
Jeremy M Cohen, Elan Rosenfeld, and J Zico Kolter · 2019
Cited alongside, same era.
Functional adversarial attacks, 2019
Cassidy Laidlaw and Soheil Feizi · 2019
Cited alongside, same era.
On physical adversarial patches for object detection, 2019
Mark Lee and Zico Kolter · 2019
Cited alongside, same era.
Enhancing gradient-based attacks with symbolic intervals, 2019
Shiqi Wang, Yizheng Chen, Ahmed Abdou, and Suman Jana · 2019
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Wasserstein adversarial examples via projected sinkhorn iterations
Eric Wong, Frank R Schmidt, and J Zico Kolter · 2019
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Making an invisibility cloak: Real world adversarial attacks on object detectors
Zuxuan Wu, Ser-Nam Lim, Larry Davis, and Tom Goldstein · 2019
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Towards stable and efficient training of verifiably robust neural networks
Huan Zhang, Hongge Chen, Chaowei Xiao, Bo Li, Duane Boning, and Cho-Jui Hsieh · 2019
Later among the works it cites.